PI Doc MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@PI Doc MCPWhat authentication methods does PI Web API support?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
PI Doc MCP
Ground your AI answers in live AVEVA PI System documentation.
What it does
PI Doc MCP is a Model Context Protocol (MCP) server that gives AI assistants like Claude direct access to the official AVEVA PI System documentation. Instead of relying on training data that may be outdated or incorrect, the AI fetches real answers from live docs — eliminating hallucination on PI-specific topics.
Results are scoped strictly to docs.aveva.com/category/pi-system. Other AVEVA product families (System Platform, CONNECT, etc.) are excluded by design.
Related MCP server: Documentation Retrieval MCP Server (DOCRET)
How it works
The server proxies the publicly accessible docs-be.aveva.com API in real time. No API key, no local doc files, no indexing step — every search and page fetch goes directly to AVEVA's documentation backend and returns content that is always up to date.
Prerequisites
Python 3.11 or later — python.org/downloads
uv — fast Python package manager
curl -LsSf https://astral.sh/uv/install.sh | shClaude Code CLI — installation guide
Installation
1. Clone the repository
git clone https://github.com/naufal-halal/pi-doc-mcp.git
cd pi-doc-mcp2. Install dependencies
uv sync3. Register with Claude Code
claude mcp add pi-docs --scope user -- uv run --directory /path/to/pi-doc-mcp python server.pyReplace /path/to/pi-doc-mcp with the absolute path to the cloned folder.
4. Restart Claude Code to pick up the new MCP server. You should see pi-docs listed when you run:
claude mcp listAvailable Tools
Once registered, Claude has access to three tools:
Tool | Description |
| Search PI System docs by keyword. Optional: |
| Fetch the full text of a documentation page by URL. Optional: |
| List all 90+ PI System documentation bundles grouped by product area (PI Server, PI Web API, Interfaces, Connectors, etc.). |
Usage Examples
Ask Claude questions like:
"What authentication methods does PI Web API support?"
"What are the required tag attributes for the PI RDBMS Interface?"
"How do I configure buffering for a PI Interface on an interface node?"
"Show me the AF SDK getting started guide."
"List all available PI System documentation bundles."
Claude will search the live docs and cite the exact page it used.
Scope
This server covers documentation bundles under docs.aveva.com/category/pi-system, including:
PI Server (Windows and Linux)
PI Web API and AF SDK
PI Vision, PI DataLink, PI Manual Logger
PI Interfaces (OPC DA/HDA, RDBMS, UFL, Modbus, DNP3, Batch, and more)
PI Connectors (OPC UA, MQTT, BACnet, IEC 61850, and more)
Adapters for Edge Data Store
PI Integrators, PI SQL / OLEDB, PI OPC UA Server
Use list_pi_bundles inside Claude to see the full list.
References
Disclaimer
This project is not affiliated with, endorsed by, or supported by AVEVA. It proxies AVEVA's publicly accessible documentation API for personal and developer use. Users are responsible for complying with AVEVA's terms of use.
License
MIT © Naufal — see LICENSE
Available Tools
2 toolsget_pageB
Fetch the text of a PI System documentation page by URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | docs.aveva.com page URL from search_pi_docs | |
| max_chars | No | Max characters to return (default 4000, max 12000) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description only says 'Fetch the text' without stating read-only nature, potential errors, or side effects. Minimal behavioral insight beyond the obvious.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One concise sentence, front-loaded with the verb. No redundant information. Efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple fetch tool, but lacks details about return format, error handling, and whether the text is plain or formatted. Could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already describes both parameters. The description adds no additional meaning beyond 'by URL'. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Fetch', the resource 'text of a PI System documentation page', and the mechanism 'by URL'. It distinguishes from siblings like search_pi_docs and list_pi_bundles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool or when not to. The schema mentions the URL comes from search_pi_docs, but the description itself lacks context or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_pi_docsA
Search AVEVA PI System documentation (scoped to docs.aveva.com/category/pi-system). Returns titles, URLs, and excerpts.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search terms, e.g. 'Kerberos authentication' or 'configure PI Interface buffering' | |
| bundle | No | Optional bundle ID to restrict search, e.g. 'pi-web-api', 'af-sdk', 'pi-server-f' | |
| n_results | No | Results to return (default 5, max 20) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool returns titles, URLs, and excerpts, which implies read-only behavior. However, it does not mention authentication requirements, rate limits, or any other operational constraints. The scope information is helpful, but more detail on behavioral traits would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that front-loads the purpose and includes key details (scope, return types). Every word serves a purpose, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 3 parameters and no output schema, the description covers the core functionality and return structure. It does not mention pagination or result ordering, but the n_results parameter handles some of that. The sibling tools are not addressed, but the description is sufficiently complete for an agent to understand the tool's primary use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter having a description in the schema. The tool description does not add extra semantic information beyond what the schema already provides. Baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search', the resource 'AVEVA PI System documentation', and the scope ('scoped to docs.aveva.com/category/pi-system'). It also lists the return fields (titles, URLs, excerpts), which distinguishes it from sibling tools like get_page (likely fetches a specific page) and list_pi_bundles (lists bundles).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used for searching documentation, but provides no explicit guidance on when to use this tool versus alternatives (get_page, list_pi_bundles). There are no 'when to use', 'when not to use', or 'see also' statements, leaving the agent to infer from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.1.0- First observed
get_page - First observed
search_pi_docs
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: search returns a list of relevant pages, while get_page retrieves the full text of a specific page. There is no overlap or ambiguity.
Both tools follow a consistent verb_noun pattern: 'search_pi_docs' and 'get_page'. The naming is predictable and clear.
With only two tools, the set is minimal but appropriate for a documentation server focused on search and retrieval. It covers the core operations without being overbearing.
The set provides search and full-text retrieval, which are the primary operations for a documentation server. Missing features like browsing by category or listing all pages are minor gaps that do not critically hinder agents.
Maintenance
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